Instructions to use simecek/DNADeberta2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use simecek/DNADeberta2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="simecek/DNADeberta2")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("simecek/DNADeberta2") model = AutoModelForMaskedLM.from_pretrained("simecek/DNADeberta2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download training_args.bin from simecek/DNADeberta2: direct link, hf CLI and curl.
- Browser
- Download file 3.18 kB
-
https://huggingface.co/simecek/DNADeberta2/resolve/main/training_args.bin
- Command line
-
hf download hf://simecek/DNADeberta2/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/simecek/DNADeberta2/resolve/main/training_args.bin
3.18 kB
- Xet hash:
- ab03818d1c9faa70ebd3edbc873ff58c630b6687708edeecf7d630512f59b855
- Size of remote file:
- 3.18 kB
- SHA256:
- 4ec178e24d92a74b86149c368b61ffec36da8333d8cdf29f0c0e44805bb74826
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.